Method and apparatus for outputting information

Abstract

Embodiments of the present disclosure disclose a method and apparatus for outputting information. A specific embodiment of the method includes: acquiring at least one personal attribute characteristic of a target user; determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute; and outputting the determined user type. This embodiment effectively utilizes the personal attribute characteristic of the user to predict the user type of the user under the preset attribute, and improves the content richness of the information output.

What is claimed is: 
     
         1 . A method for outputting information, the method comprising:
 acquiring at least one personal attribute characteristic of a target user;   determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute; and   outputting the determined user type.   
     
     
         2 . The method according to  claim 1 , wherein the at least one personal attribute characteristic comprises at least one of: a natural personal attribute characteristic or a network behavior characteristic, and the network behavior characteristic comprises at least one of: an electronic map navigation characteristic, an interests profile characteristic, an address characteristic, a common application characteristic, a credit score characteristic or a network search topic characteristic. 
     
     
         3 . The method according to  claim 2 , wherein the determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute comprises:
 importing the acquired at least one personal attribute characteristic into a pre-trained user type determination model to obtain the user type of the target user under the preset attribute, wherein the user type determination model is used to represent a corresponding relationship between the at least one personal attribute characteristic and the user type.   
     
     
         4 . The method according to  claim 2 , wherein the user type comprises a first user type and a second user type. 
     
     
         5 . The method according to  claim 4 , wherein the determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute comprises:
 importing the acquired at least one personal attribute characteristic into a pre-trained vehicle accident occurrence frequency calculation model to obtain a predicted vehicle accident occurrence frequency of the target user, wherein the vehicle accident occurrence frequency calculation model is used to represent a corresponding relationship between the at least one personal attribute characteristic and a vehicle accident occurrence frequency;   determining the user type of the target user under the preset attribute to be the first user type, in response to determining the predicted vehicle accident occurrence frequency being greater than a preset vehicle accident occurrence frequency threshold; and   determining the user type of the target user under the preset attribute to be the second user type, in response to determining the predicted vehicle accident occurrence frequency being not greater than the preset vehicle accident occurrence frequency threshold.   
     
     
         6 . The method according to  claim 4 , wherein the determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute comprises:
 importing the acquired at least one personal attribute characteristic into a pre-trained vehicle accident compensation rate calculation model to obtain a predicted vehicle accident compensation rate of the target user, wherein the vehicle accident compensation rate calculation model is used to represent a corresponding relationship between the at least one personal attribute characteristic and a vehicle accident compensation rate;   determining the user type of the target user under the preset attribute to be the first user type, in response to determining the predicted vehicle accident compensation rate being greater than a preset vehicle accident compensation rate threshold; and   determining the user type of the target user under the preset attribute to be the second user type, in response to determining the predicted vehicle accident compensation rate being not greater than the preset vehicle accident compensation rate threshold.   
     
     
         7 . The method according to  claim 3 , wherein the user type determination model is trained and obtained by:
 acquiring an initial user type determination model and a predetermined first sample data set, wherein each piece of sample data in the first sample data set comprises at least one personal attribute characteristic of a user and a user type of the user under the preset attribute;   using the at least one personal attribute characteristic of the user in each piece of sample data in the first sample data set as input data, and the user type of the user under the preset attribute in the sample data as corresponding output data to train the initial user type determination model using a machine learning method; and   defining the trained initial user type determination model as the pre-trained user type determination model.   
     
     
         8 . The method according to  claim 5 , wherein the vehicle accident occurrence frequency calculation model is trained and obtained by:
 acquiring an initial vehicle accident occurrence frequency calculation model and a predetermined second sample data set, wherein each piece of sample data in the second sample data set comprises at least one personal attribute characteristic of a user and a historical vehicle accident occurrence frequency of the user;   using the at least one personal attribute characteristic of the user in each piece of sample data in the second sample data set as input data, and the historical vehicle accident occurrence frequency of the user in the sample data as corresponding output data to train the initial vehicle accident occurrence frequency calculation model using a machine learning method; and   defining the trained initial vehicle accident occurrence frequency calculation model as the pre-trained vehicle accident occurrence frequency calculation model.   
     
     
         9 . The method according to  claim 6 , wherein the vehicle accident compensation rate calculation model is trained and obtained by:
 acquiring an initial vehicle accident compensation rate calculation model and a predetermined third sample data set, wherein each piece of sample data in the third sample data set comprises at least one personal attribute characteristic of a user and a historical vehicle accident compensation rate of the user;   using the at least one personal attribute characteristic of the user in each piece of sample data in the third sample data set as input data, and the historical vehicle accident compensation rate of the user in the sample data as corresponding output data to train the initial vehicle accident compensation rate calculation model using a machine learning method; and   defining the trained initial vehicle accident compensation rate calculation model as the pre-trained vehicle accident compensation rate calculation model.   
     
     
         10 . An apparatus for outputting information, the apparatus comprising:
 at least one processor; and   a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:   acquiring at least one personal attribute characteristic of a target user;   determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute; and   outputting the determined user type.   
     
     
         11 . The apparatus according to  claim 10 , wherein the at least one personal attribute characteristic comprises at least one of: a natural personal attribute characteristic or a network behavior characteristic, and the network behavior characteristic comprises at least one of: an electronic map navigation characteristic, an interests profile characteristic, an address characteristic, a common application characteristic, a credit score characteristic or a network search topic characteristic. 
     
     
         12 . The apparatus according to  claim 11 , wherein the determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute comprises:
 importing the acquired at least one personal attribute characteristic into a pre-trained user type determination model to obtain the user type of the target user under the preset attribute, wherein the user type determination model is used to represent a corresponding relationship between the at least one personal attribute characteristic and the user type.   
     
     
         13 . The apparatus according to  claim 11 , wherein the user type comprises a first user type and a second user type. 
     
     
         14 . The apparatus according to  claim 13 , wherein the determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute comprises:
 importing the acquired at least one personal attribute characteristic into a pre-trained vehicle accident occurrence frequency calculation model to obtain a predicted vehicle accident occurrence frequency of the target user, wherein the vehicle accident occurrence frequency calculation model is used to represent a corresponding relationship between the at least one personal attribute characteristic and a vehicle accident occurrence frequency;   determining the user type of the target user under the preset attribute to be the first user type, in response to determining the predicted vehicle accident occurrence frequency being greater than a preset vehicle accident occurrence frequency threshold; and   determining the user type of the target user under the preset attribute to be the second user type, in response to determining the predicted vehicle accident occurrence frequency being not greater than the preset vehicle accident occurrence frequency threshold.   
     
     
         15 . The apparatus according to  claim 13 , wherein the determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute comprises:
 importing the acquired at least one personal attribute characteristic into a pre-trained vehicle accident compensation rate calculation model to obtain a predicted vehicle accident compensation rate of the target user, wherein the vehicle accident compensation rate calculation model is used to represent a corresponding relationship between the at least one personal attribute characteristic and a vehicle accident compensation rate;   determining the user type of the target user under the preset attribute to be the first user type, in response to determining the predicted vehicle accident compensation rate being greater than a preset vehicle accident compensation rate threshold; and   determining the user type of the target user under the preset attribute to be the second user type, in response to determining the predicted vehicle accident compensation rate being not greater than the preset vehicle accident compensation rate threshold.   
     
     
         16 . The apparatus according to  claim 12 , wherein the user type determination model is trained and obtained by:
 acquiring an initial user type determination model and a predetermined first sample data set, wherein each piece of sample data in the first sample data set comprises at least one personal attribute characteristic of a user and a user type of the user under the preset attribute;   using the at least one personal attribute characteristic of the user in each piece of sample data in the first sample data set as input data, and the user type of the user under the preset attribute in the sample data as corresponding output data to train the initial user type determination model using a machine learning method; and   defining the trained initial user type determination model as the pre-trained user type determination model.   
     
     
         17 . The apparatus according to  claim 14 , wherein the vehicle accident occurrence frequency calculation model is trained and obtained by:
 acquiring an initial vehicle accident occurrence frequency calculation model and a predetermined second sample data set, wherein each piece of sample data in the second sample data set comprises at least one personal attribute characteristic of a user and a historical vehicle accident occurrence frequency of the user;   using the at least one personal attribute characteristic of the user in each piece of sample data in the second sample data set as input data, and the historical vehicle accident occurrence frequency of the user in the sample data as corresponding output data to train the initial vehicle accident occurrence frequency calculation model using a machine learning method; and   defining the trained initial vehicle accident occurrence frequency calculation model as the pre-trained vehicle accident occurrence frequency calculation model.   
     
     
         18 . The apparatus according to  claim 15 , wherein the vehicle accident compensation rate calculation model is trained and obtained by:
 acquiring an initial vehicle accident compensation rate calculation model and a predetermined third sample data set, wherein each piece of sample data in the third sample data set comprises at least one personal attribute characteristic of a user and a historical vehicle accident compensation rate of the user;   using the at least one personal attribute characteristic of the user in each piece of sample data in the third sample data set as input data, and the historical vehicle accident compensation rate of the user in the sample data as corresponding output data to train the initial vehicle accident compensation rate calculation model using a machine learning method; and   defining the trained initial vehicle accident compensation rate calculation model as the pre-trained vehicle accident compensation rate calculation model.   
     
     
         19 . A non-transitory computer storage medium storing a computer program, the computer program when executed by one or more processors, causes the one or more processors to perform operations, the operations comprising:
 acquiring at least one personal attribute characteristic of a target user;   determining, based on the acquired at least one personal attribute characteristic, a user type of the target user under a preset attribute; and   outputting the determined user type.